Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

TLDR

The study integrates a cascade‑of‑rejectors approach with Histograms of Oriented Gradients features to create a fast, accurate human detection system. The system uses variable‑size HoG blocks, selects them via AdaBoost, and employs integral images with a rejection cascade to accelerate detection. On 320×280 images, the detector runs at 5–30 frames per second while achieving accuracy comparable to state‑of‑the‑art methods.

Abstract

We integrate the cascade-of-rejectors approach with the Histograms of Oriented Gradients (HoG) features to achieve a fast and accurate human detection system. The features used in our system are HoGs of variable-size blocks that capture salient features of humans automatically. Using AdaBoost for feature selection, we identify the appropriate set of blocks, from a large set of possible blocks. In our system, we use the integral image representation and a rejection cascade which significantly speed up the computation. For a 320 × 280 image, the system can process 5 to 30 frames per second depending on the density in which we scan the image, while maintaining an accuracy level similar to existing methods.